Prediction of Visual Quality Metrics in Lossy Image Compression
| Autor: | Fangfang Li, Vladimir V. Lukin, Benoit Vozel, O. Krylova, Sergey Krivenko |
|---|---|
| Přispěvatelé: | National Aerospace University, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Lossy compression 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine lossy compression 0202 electrical engineering electronic engineering information engineering Discrete cosine transform [CHIM]Chemical Sciences Quality (business) Computer vision image Image resolution media_common Pixel business.industry metric prediction visual quality Compression ratio Metric (mathematics) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 40th IEEE International Conference on Electronics and Nanotechnology, ELNANO 2020 40th IEEE International Conference on Electronics and Nanotechnology, ELNANO 2020, Apr 2020, Kyiv, Ukraine. pp.478-483, ⟨10.1109/ELNANO50318.2020.9088819⟩ |
| DOI: | 10.1109/elnano50318.2020.9088819 |
| Popis: | International audience; Images of different origin are widely used nowadays in various applications including medical diagnostic systems, remote sensing, etc. Due to modern tendency to improve imaging system resolution and increase image size, it has often become necessary to compress images before their storage and transferring via communication lines. Lossy compression is mostly employed for this purpose and an important task for it is to find and provide an appropriate compromise between compression ratio and quality of compressed data, in the first order, image visual quality. This paper considers an approach to predicting visual quality characterized by the metrics MSEHVSM or, equivalently, PSNR-HVS-M for the coder AGU based on discrete cosine transform (DCT). It is demonstrated that it is possible to estimate MSEHVSM in a limited number of 8×8 pixel blocks and then to predict this metric for the entire image for the considered coder. The influence of image content and the number of analyzed blocks is studied. It is shown that 500 or 1000 blocks are usually enough to carry out prediction with appropriate accuracy. © 2020 IEEE. |
| Databáze: | OpenAIRE |
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